Advertisement

Mitigating Insider Threat on Database Integrity

  • Weihan Li
  • Brajendra Panda
  • Qussai Yaseen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7671)

Abstract

We have developed a model to predict and prevent potential damage caused by malicious transactions in a database system. The model consists of a number of rules sets that constrain the relationships among data items and transactions. It uses a graph called Predictive Dependency Graph to determine data flow patterns among data items. The model offers a mechanism to monitor suspicious insiders activities and potential harm to the database. Through simulation we have tested the effectiveness of the model. The results show the effectiveness of the proposed model in predicting damage that can occur by malicious transactions.

Keywords

Insider Threat Malicious Transactions Database Systems Security 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Mills, R.F., Peterson, G.L., Grimaila, M.R.: In: Knapp, K.J. (ed.) Cyber Security and Global Information Assurance: Threat Analysis and Response Solution, U.S. Air Force Academy, Colorado, USA (2009)Google Scholar
  2. 2.
    Clark, D., Wilson, D.: A comparison of Commercial and Military Computer Security Policies. In: IEEE Symposium on Security & Privacy (1987)Google Scholar
  3. 3.
    Chung, C., Gertz, M., Levitt, K.: Demids: A misuse detection system for database systems. In: 14th IFIP WG11.3 Working Conference on Database and Application Security (2000)Google Scholar
  4. 4.
    Lee, S.Y., Low, W.L., Wong, P.Y.: Learning Fingerprints for a Database Intrusion Detection System. In: Gollmann, D., Karjoth, G., Waidner, M. (eds.) ESORICS 2002. LNCS, vol. 2502, pp. 264–279. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  5. 5.
    Kamra, A., Bertino, E., Terzi, E.: Detecting anomalous access patterns in relational databases. J. VLDB 17(5), 1063–1077 (2008)CrossRefGoogle Scholar
  6. 6.
    Hu, Y., Panda, B.: Design and Analysis of Techniques for Detection of Malicious Activities in Database System. J. Network and System Management 13(3), 111–125 (2005)Google Scholar
  7. 7.
    Lee, W., Stolfo, S.J.: A framework for constructing features and models for intrusion detection system. ACM Transactions on Information and System Security 3(4), 227–261 (2000)CrossRefGoogle Scholar
  8. 8.
    Meng, Y., Liu, P., Zang, W.: Multi-Version Attack Recovery for Workflow Systems. In: 19th Annual Computer Security Applications Conference (2003)Google Scholar
  9. 9.
    Srivastava, A., Sural, S., Majumdar, A.K.: Weighted Intra-transactional Rule Mining for Database Intrusion Detection. In: Ng, W.-K., Kitsuregawa, M., Li, J., Chang, K. (eds.) PAKDD 2006. LNCS (LNAI), vol. 3918, pp. 611–620. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Ray, I., Poolsapassit, N.: Using Attack Trees to Identify Malicious Attacks from Authorized Insiders. In: De Capitani di Vimercati, S., Syverson, P.F., Gollmann, D. (eds.) ESORICS 2005. LNCS, vol. 3679, pp. 231–246. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Martinez-Moyano, I., Rich, E., Conrad, S., Anderson, D.F., Stewart, T.R.: A Behavioral Theory of Insider-Threat Risk: A System Dynamic Approach. ACM Transactions on Modeling and Computer Simulation 18(2) (2008)Google Scholar
  12. 12.
    Yaseen, Q., Panda, B.: Predicting and Preventing Insider Threat in Relational Database Systems. In: Samarati, P., Tunstall, M., Posegga, J., Markantonakis, K., Sauveron, D. (eds.) WISTP 2010. LNCS, vol. 6033, pp. 368–383. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Yaseen, Q., Panda, B.: Malicious Modification attacks by Insiders in Relational Databases: Prediction and Prevention. In: 2nd IEEE International Conference on Information Privacy, Security, Risk and Trust (2010)Google Scholar
  14. 14.
    Newman, A.: Database Activity Monitoring: Intrusion Detection & Security Auditing. DAM Whitepaper, http://www.appsecinc.com/presentations/DAM_wp82305.pdf
  15. 15.
    Mogull, R.: Understanding and Selecting a Database Activity Monitoring Solution, http://www.securosis.com/reports/DAM-Whitepaper-final.pdf

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Weihan Li
    • 1
  • Brajendra Panda
    • 1
  • Qussai Yaseen
    • 2
  1. 1.Department of Computer Science and Computer EngineeringUniversity of ArkansasFayettevilleUSA
  2. 2.Department of Computer ScienceYarmouk UniversityIrbidJordan

Personalised recommendations